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I am currently taking a nonlinear optimization course and the text is Bertsekas' "Nonlinear Programming 2e".

I think the book does a decent job but I am a much more "hands-on" and visual learner so I'm looking for an additional book with worked examples, visuals explaining concepts, etc.

I've done some searching myself but I thought I'd get some recommendations from real mathematicians-- or people that share my learning disability. :)

Thanks!

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I like Boyd & Vandenberghe's Convex optimization. Generally I think convex analysis provides good intuition and is usually amenable to nice pictures. –  copper.hat Oct 9 '12 at 23:28

1 Answer 1

Some good books on nonlinear optimization:

1. Boris T. Polyak, Introduction to Optimization

The field of nonlinear optimization has benefited from several important ideas developed in the Soviet Union. Some of these ideas underwent a parallel development in the West, but others received inadequate attention in English language textbooks. For this reason the publication of the present book by a principal Soviet contributor is particularly valuable. It represents what is probably the first comprehensive synthesis of the nonlinear programming methodologies that are popular in the West and the Soviet Union. The reader will find here a systematic treatment of both classical subjects, and topics little covered elsewhere—such as nondifferentiable optimization, degenerate problems, and stochastic optimization methods. Beyond this, however, this text has many significant merits. It gives careful attention to both mathematical rigor and practical relevance. The convergence analysis of numerical methods is done in a unified manner. A systematic effort is made to chart the limits of the methodology by providing performance analysis on difficult problems. There is a thoughtful discussion of the practical solution process. A wealth of new or little known material is included in the text and the exercises. Above all, the book is written by a true expert with a refined understanding of the nature, purpose, and limitations of nonlinear optimization and applied mathematics in general.

2. Olvi L. Mangasarian, Nonlinear Programming

Twenty-five years have passed since the original edition of this book appeared; however, the topics covered are still timely and currently taught at the University of Wisconsin as well as many other major institutions. At Wisconsin these topics are taught in a course jointly listed by the Computer
Sciences, Industrial Engineering, and Statistics departments. Students from these and other disciplines regularly take this course. Each year I get a number of requests from the United States and abroad for copies of the book and for permission to reproduce reserve copies for libraries. I was therefore pleased when SIAM approached me with a proposal to reprint the book in its Classics series. I believe that this book is an appropriate choice for this series inasmuch as it is a concise, igorous, yet accessible account o ' the fundamentals of constrained optimization theory that is useful to both the beginning student as well as the active researcher. I am appreciative that SIAM has chosen to publish the book and to make the corrections that I
supplied. I am especially grateful to Vickie Kearn and Ed Block for their friendly and professional handling of the publication process. My hope is that the mathematical
programming community will benefit from this endeavor.

3. David G. Luenberger, Linear and Nonlinear Programming

This book is intended as a text covering the central concepts of practical optimization techniques. It is designed for either self-study by professionals or classroom work at the undergraduate or graduate level for students who have a technical background in engineering, mathematics, or science. Like the field of optimization itself, which involves many classical disciplines, the book should be useful to system analysts, operations researchers, numerical analysts, management scientists, and other specialists from the host of disciplines from which practical optimization applications are drawn. The prerequisites for convenient use of the book are relatively modest; the prime requirement being some familiarity with introductory elements of linear algebra. Certain sections and developments do assume some knowledge of more advanced concepts of linear algebra, such as eigenvector analysis, or some background in sets of real numbers, but the text is structured so that the mainstream of the development can be faithfully pursued without reliance on this more advanced background material. Although the book covers primarily material that is now fairly standard, it is intended to reflect modern theoretical insights. These provide structure to what might otherwise be simply a collection of techniques and results, and this is valuable both as a means for learning existing material and for developing new results. One major insight of this type is the connection between the purely analytical character of an optimization problem, expressed perhaps by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. This was a major theme of the first edition of this book and the second edition expands and further illustrates this relationship. As in the second edition, the material in this book is organized into three separate parts. Part I is a self-contained introduction to linear programming, a key component of optimization theory. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. It is possible to go directly into Parts II and III omitting Part I, and, in fact, the book has been used in this way in many universities. Each part of the book contains enough material to form the basis of a one-quarter course. In either classroom use or for self-study, it is important not to overlook the suggested exercises at the end of each chapter. The selections generally include exercises of a computational variety designed to test one’s understanding of a particular algorithm, a theoretical variety designed to test one’s understanding of a given theoretical development, or of the variety that extends the presentation of the chapter to new applications or theoretical areas. One should attempt at least four or five exercises from each chapter. In progressing through the book it would be unusual to read straight through from cover to cover. Generally, one will wish to skip around. In order to facilitate this mode, we have indicated sections of a specialized or digressive nature with an asterisk∗. There are several features of the revision represented by this third edition. In Part I a new Chapter 5 is devoted to a presentation of the theory and methods of polynomial-time algorithms for linear programming. These methods include, especially, interior point methods that have revolutionized linear programming. The first part of the book can itself serve as a modern basic text for linear programming. Part II includes an expanded treatment of necessary conditions, manifested by not only first- and second-order necessary conditions for optimality, but also by zeroth-order conditions that use no derivative information. This part continues to present the important descent methods for unconstrained problems, but there is new material on convergence analysis and on Newton’s methods which is frequently used as the workhorse of interior point methods for both linear and nonlinear programming. Finally, Part III now includes the global theory of necessary conditions for constrained problems, expressed as zero-th order conditions. Also interior point methods for general nonlinear programming are explicitly discussed within the sections on penalty and barrier methods. A significant addition to Part III is an expanded presentation of duality from both the global and local perspective. Finally, Chapter 15, on primal–dual methods has additional material on interior point methods and an introduction to the relatively new field of semidefinite programming, including several examples. We wish to thank the many students and researchers who over the years have given us comments concerning the second edition and those who encouraged us to carry out this revision.

4. A. D. Ioffe, V. M. Tikhomirov, Theory of Extremal Problems

In recent years, the efforts of many mathematicians have been directed toward investigating the subject of extremal problems from a unified viewpoint, selecting common features in the methods for treating these problems, and developing the necessary mathematical apparatus. As a result, it has become possible to speak of the development of the general theory of extremal problems. Three topics in this theory have now acquired fully developed outlines. These are the questions of the mathematical foundations of the theory, the necessary conditions for an extremum, and - although to a lesser extent - the existence of solutions. The part of the theory that is devoted to numerical optimization methods is rapidly developing. The theory of sufficient conditions has not yet reached its finished stage, although there too certain results of a general nature have been obtained. In this book, we tbuch upon all of the above mentioned basic topics in the theory of extremal problems except numerical methods. The first five chapters of the book are devoted to the mathematical apparatus and to necessary conditions for an extremum. Sufficient conditions are studied in the seventh chapter. In the eighth and ninth chapters, we present theorems on the existence of solutions of problems in the calculus of variations and optimal control, together with the mathematical apparatus necessary for the proofs of these theorems. In the sixth chapter, we derive from the general theory developed in the preceding chapters consequences for classes of problems with special structure, namely, for linear, convex, quadratic, and discrete problems. The last, tenth chapter is devoted to certain applications of the theory to the solution of specific problems. We have also included a separate collection of problems, many of which are given with solutions In choosing the material, we did not try to encompass all the latest results. The main purpose of the book is to explain and review the methods that have arisen in the theory of extremal problems, and to show how these methods are applied in particular areas, such as mathematical programming, calculus of variations and optimal control. The initial stimulus to create a general theory of extremal problems was provided by the efforts to place the Pontrjagin maximum principle within an abstract framework. These efforts brought about the investigation of problems with a mixed, partly smooth and partly convex, structure. In this book, problems of this type are investigated in detail. There are also other possible approaches to these problems, and time will show which of them will prove to be most fruitful. This book is intended primarily for graduate and postgraduate students, and for scientists who work in the area of solving optimization problems. In writing it, we have utilized the experience of teaching in the Department of Mathematics and Mechanics at Moscow State University. We hope that the book can be used as a text in VF-;OUS courses related to optimization given in universities and technical colleges. In order to fully understand all parts of the book, one should have a university course in functional analysis, but much of its contents are intended for a broader audience. The point is that the formulations of a majority of the most important theorems, which give recipies for solving problems, can be mastered with substantially less mathematical background than required for the understanding of proofs. Therefore, we have tried to organize the presentation in such a way that the formulations of the main results, the general remarks concerning them, and the solutions of prablems, be separate from the proofs. It is not necessary to read the chapters in their given order. Many subjects can be studied independently of the other material. Let us indicate several such possibilities. Sections 2.1, 2.2,2.4, and 6.4 form an elementary course in the calculus of variations. In essence, these sections are based only on facts from classical analysis. An initial course in linear and convex programming is contained in 000.3, 1.3, 6.1, and 7.3. Chapter 1 and 007.1-7.3 are devoted to the subject “Necessary and sufficient conditions for an extremum in minimization problems in linear spaces.” Sections 2.1-2.3, 6.3, 7.4, 10.4, and Subsection 9.2.4 form an extended course in the calculus of variations, although insufficient attention is given here to Hamilton-Jacobi theory. In Chapter 8 and 449.1 a d 9.2, we treat the questions of the existence of a solution to optimal control problems. Chapter 8 together with 49.3 gives an idea of duality methods in optimal control theory. Chapters 1-5, 8, and 9 form an extended course in optimal control theory. Finally, Chapters 3, 4, and 8 contain a reasonably detailed introduction to infinite-dimensional convex analysis. At first, we planned to include in the book a number of other subjects, such as extensions of variational problems, sliding and singular regimes, and numerical methods. However, we have been unable to treat these matters for lack of space. We should also mention several other problems of importance which are not touched upon in the book. These concern primarily the relationships between the calculus of variations and classical mechanics, Hamilton-Jacobi theory, and dynamic programming. In the remarks which conclude the Introduction, we indicate monographs and articles that pertain to subjects which remain outside the scope of this book. In conclusion, we would like to express our gratitude to B.T. Poljak, who kindly provided certain materials that helped in the development of our approach to the classification of extremal problems. Our thanks go to M.A. Krasnosel’skii, who looked over the manuscri,pt and made a number of useful suggestions, and to V.M. Alekseev and S.V. Fomin, with whom we discussed various matters related to the presentation of the material. Numerous discusdons with A.A. Miljutin were of great importance to us. We are grateful to A.V. Barykin, B. Luderer, G.G. Magaril-Il’jaev, E.S. Polovinkin, M.A. Rvachev, V.M. Safro, M.I. Stesin, and to M. Tagiev, who helped us with the preparation of the manuscript.

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-1 This is not a book recommendation. –  user53153 Jan 18 '13 at 2:06

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